Steve Furber

Bio: Steve Furber is an academic researcher from University of Manchester. The author has contributed to research in topics: Spiking neural network & Neuromorphic engineering. The author has an hindex of 42, co-authored 260 publications receiving 9657 citations. Previous affiliations of Steve Furber include University of Cambridge & Beijing University of Technology.

The SpiNNaker Project

Abstract: The spiking neural network architecture (SpiNNaker) project aims to deliver a massively parallel million-core computer whose interconnect architecture is inspired by the connectivity characteristics of the mammalian brain, and which is suited to the modeling of large-scale spiking neural networks in biological real time. Specifically, the interconnect allows the transmission of a very large number of very small data packets, each conveying explicitly the source, and implicitly the time, of a single neural action potential or “spike.” In this paper, we review the current state of the project, which has already delivered systems with up to 2500 processors, and present the real-time event-driven programming model that supports flexible access to the resources of the machine and has enabled its use by a wide range of collaborators around the world.

Principles of Asynchronous Circuit Design: A Systems Perspective

Abstract: Principles of Asynchronous Circuit Design - A Systems Perspective addresses the need for an introductory text on asynchronous circuit design. Part I is an 8-chapter tutorial which addresses the most important issues for the beginner, including how to think about asynchronous systems. Part II is a 4-chapter introduction to Balsa, a freely-available synthesis system for asynchronous circuits which will enable the reader to get hands-on experience of designing high-level asynchronous systems. Part III offers a number of examples of state-of-the-art asynchronous systems to illustrate what can be built using asynchronous techniques. The examples range from a complete commercial smart card chip to complex microprocessors. The objective in writing this book has been to enable industrial designers with a background in conventional (clocked) design to be able to understand asynchronous design sufficiently to assess what it has to offer and whether it might be advantageous in their next design task.

Overview of the SpiNNaker System Architecture

Abstract: SpiNNaker (a contraction of Spiking Neural Network Architecture) is a million-core computing engine whose flagship goal is to be able to simulate the behavior of aggregates of up to a billion neurons in real time. It consists of an array of ARM9 cores, communicating via packets carried by a custom interconnect fabric. The packets are small (40 or 72 bits), and their transmission is brokered entirely by hardware, giving the overall engine an extremely high bisection bandwidth of over 5 billion packets/s. Three of the principal axioms of parallel machine design (memory coherence, synchronicity, and determinism) have been discarded in the design without, surprisingly, compromising the ability to perform meaningful computations. A further attribute of the system is the acknowledgment, from the initial design stages, that the sheer size of the implementation will make component failures an inevitable aspect of day-to-day operation, and fault detection and recovery mechanisms have been built into the system at many levels of abstraction. This paper describes the architecture of the machine and outlines the underlying design philosophy; software and applications are to be described in detail elsewhere, and only introduced in passing here as necessary to illuminate the description.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

A million spiking-neuron integrated circuit with a scalable communication network and interface

Abstract: Inspired by the brain’s structure, we have developed an efficient, scalable, and flexible non–von Neumann architecture that leverages contemporary silicon technology. To demonstrate, we built a 5.4-billion-transistor chip with 4096 neurosynaptic cores interconnected via an intrachip network that integrates 1 million programmable spiking neurons and 256 million configurable synapses. Chips can be tiled in two dimensions via an interchip communication interface, seamlessly scaling the architecture to a cortexlike sheet of arbitrary size. The architecture is well suited to many applications that use complex neural networks in real time, for example, multiobject detection and classification. With 400-pixel-by-240-pixel video input at 30 frames per second, the chip consumes 63 milliwatts.